Biology subjects

Cottaris, N. P.

Publications and source records attributed to Cottaris, N. P..

6 recordsLinked to original sources

bioRxiv2018

The relative contribution of color and material in object selection

Information about color, material, texture and shape guide how we interact with objects. We developed a paradigm that quantifies how two object properties (color and material) combine in object selection. On each experimental trial, observers viewed three blob-shaped objects -- the target and two tests -- and selected the test that was more similar to the target. Across trials the target was fixed while the tests varied in color and material. We present a novel observer model that allows us to describe observers selection data in terms of (1) the underlying perceptual stimulus representation and (2) a color-material weight, which quantifies the relative importance of color vs. material in selection. We document large individual differences in the color-material weight. Furthermore, our analyses reveal limits on how precisely selection data simultaneously constrain perceptual representations and the color-material weight. These limits should guide future efforts towards understanding the multidimensional nature of object perception.

neuroscience↗

bioRxiv2018

Computational Luminance Constancy from Naturalistic Images

The human visual system supports stable percepts of object color even though the light that reflects from object surfaces varies significantly with the scene illumination. To understand the computations that support stable color perception, we study how estimating a target objects luminous reflectance factor (LRF; a measure of the light reflected from the object under a standard illuminant) depends on variation in key properties of naturalistic scenes. Specifically, we study how variation in target object reflectance, illumination spectra, and the reflectance of back-ground objects in a scene impact estimation of a target objects LRF. To do this, we applied supervised statistical learning methods to the simulated excitations of human cone photoreceptors, obtained from labeled naturalistic images. The naturalistic images were rendered with computer graphics. The illumination spectra of the light sources and the reflectance spectra of the surfaces in the scene were generated using statistical models of natural spectral variation. Optimally decoding target object LRF from the responses of a small learned set of task-specific linear receptive fields that operate on a contrast representation of the cone excitations yields estimates that are within 13% of the correct LRF. Our work provides a framework for evaluating how different sources of scene variability limit performance on luminance constancy.

neuroscience↗

bioRxiv2018

Computational-Observer Analysis of Illumination Discrimination

The spectral properties of the ambient illumination provide useful information about time of day and weather. We study the perceptual representation of illumination by analyzing measurements of how well people discriminate between illuminations across scene configurations. More specifically, we compare human performance to a computational-observer analysis that evaluates the information available in the isomerizations of the cones in a model human photoreceptor mosaic. Some patterns of human performance are predicted by the computational observer, other aspects are not. The analysis clarifies which aspects of performance require additional explanation in terms of the action of visual mechanisms beyond the isomerization of light by the cones.

neuroscience↗

bioRxiv2018

The perception of color and material in natural tasks

Perceived object color and material properties help us to select and interact with objects. Because there is no simple mapping between the pattern of an objects image on the retina and its physical reflectance, our perception of color and material are made more useful through sophisticated visual computations. A long-standing goal in vision science is to describe how these computations work, particularly as they act to stabilize perceived color and material against variation in scene factors extrinsic to object surface properties, such as the illumination. If we take seriously the notion that perceived color and material are useful because they help guide behavior in natural tasks, then we need experimental that measure and models that describe how they are used in such tasks. To this end, we have developed selection-based methods and accompanying perceptual models for studying perceived object color and material. This focused review highlights key aspects of our work. It includes a discussion of future directions and challenges, as well as an outline of a computational observer model that incorporates early, known, stages of visual processing and that clarifies how early vision shapes selection performance.

neuroscience↗

bioRxiv2018

Spatial summation in the human fovea: the effect of optical aberrations and fixational eye movements

Psychophysical inferences about the neural mechanisms supporting spatial vision can be undermined by uncertainties introduced by optical aberrations and fixational eye movements, particularly in fovea where the neuronal grain of the visual system is fine. We examined the effect of these pre-neural factors on photopic spatial summation in the human fovea using a custom adaptive optics scanning light ophthalmoscope that provided control over optical aberrations and retinal stimulus motion. Consistent with previous results, Riccos area of complete summation encompassed multiple photoreceptors when measured with ordinary amounts of ocular aberrations and retinal stimulus motion. When both factors were minimized experimentally, summation areas were essentially unchanged, suggesting that foveal spatial summation is limited by post-receptoral neural pooling. We compared our behavioral data to predictions generated with a physiologically-inspired front-end model of the visual system, and were able to capture the shape of the summation curves obtained with and without pre-retinal factors using a single post-receptoral summing filter of fixed spatial extent. Given our data and modeling, neurons in the magnocellular visual pathway, such as parasol ganglion cells, provide a candidate neural correlate of Riccos area in the central fovea.

neuroscience↗

bioRxiv2017

Simulation of visual perception and learning with a retinal prosthesis

The nature of artificial vision with a retinal prosthesis, and the degree to which the brain can adapt to the unnatural input from such a device, are poorly understood. Therefore, the development of current and future devices may be aided by theory and simulations that help to infer and understand what prosthesis patients see. A biologically-informed, extensible computational framework is presented here to predict visual perception and the potential effect of learning with a subretinal prosthesis. The framework relies on optimal linear reconstruction of the stimulus from retinal responses to infer the visual information available to the patient. A simulation of the physiological optics of the eye and light responses of the major retinal neurons was used to calculate the optimal linear transformation for reconstructing natural images from retinal activity. The result was then used to reconstruct the visual stimulus during the artificial activation expected from a subretinal prosthesis in a degenerated retina, as a proxy for inferred visual perception. Several simple observations reveal the potential utility of such a simulation framework. The inferred perception obtained with prosthesis activation was substantially degraded compared to the inferred perception obtained with normal retinal responses, as expected given the limited resolution and lack of cell type specificity of the prosthesis. Consistent with clinical findings and the importance of cell type specificity, reconstruction using only ON cells, and not OFF cells, was substantially more accurate. Finally, when reconstruction was re-optimized for prosthesis stimulation, simulating the greatest potential for learning by the patient, the accuracy of inferred perception was much closer to that of healthy vision. The reconstruction approach thus provides a more complete method for exploring the potential for treating blindness with retinal prostheses than has been available previously. It may also be useful for interpreting patient data in clinical trials, and for improving prosthesis design.

neuroscience↗